{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "supposed-greeting",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import bioframe\n",
    "\n",
    "import cooler\n",
    "import cooltools \n",
    "import cooltools.api.expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "included-contrary",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cooltools.sandbox\n",
    "import cooltools.sandbox.expected_smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "modified-shaft",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./test.mcool\n"
     ]
    }
   ],
   "source": [
    "# download test data\n",
    "# this file is 145 Mb, and may take a few seconds to download\n",
    "cool_file = cooltools.download_data(\"HFF_MicroC\", cache=True, data_dir='./')    \n",
    "print(cool_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "supported-trailer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load a Hi-C map at a 1kb resolution from a cooler file.\n",
    "clr = cooler.Cooler('./test.mcool::/resolutions/1000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "recovered-leone",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr2</td>\n",
       "      <td>0</td>\n",
       "      <td>93139351</td>\n",
       "      <td>chr2:0-93139351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr2</td>\n",
       "      <td>93139351</td>\n",
       "      <td>242193529</td>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr17</td>\n",
       "      <td>0</td>\n",
       "      <td>24714921</td>\n",
       "      <td>chr17:0-24714921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr17</td>\n",
       "      <td>24714921</td>\n",
       "      <td>83257441</td>\n",
       "      <td>chr17:24714921-83257441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chrom     start        end                     name\n",
       "0   chr2         0   93139351          chr2:0-93139351\n",
       "1   chr2  93139351  242193529  chr2:93139351-242193529\n",
       "2  chr17         0   24714921         chr17:0-24714921\n",
       "3  chr17  24714921   83257441  chr17:24714921-83257441"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use bioframe to fetch the genomic features from the UCSC.\n",
    "hg38_chromsizes = bioframe.fetch_chromsizes('hg38')\n",
    "hg38_cens = bioframe.fetch_centromeres('hg38')\n",
    "hg38_arms = bioframe.core.construction.add_ucsc_name_column(bioframe.make_chromarms(hg38_chromsizes,  hg38_cens))\n",
    "\n",
    "hg38_arms = hg38_arms[hg38_arms.chrom.isin(clr.chromnames)].reset_index(drop=True)\n",
    "hg38_arms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "contrary-fruit",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cvd == contacts-vs-distance\n",
    "cvd = cooltools.api.expected.diagsum_symm(\n",
    "    clr=clr,\n",
    "    view_df=hg38_arms,\n",
    "    transforms={'balanced': lambda p: p['count']*p['weight1']*p['weight2']}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6332326",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import importlib\n",
    "# importlib.reload(cooltools.sandbox.expected_smoothing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "based-vision",
   "metadata": {},
   "outputs": [],
   "source": [
    "cvd_smoothed = cooltools.sandbox.expected_smoothing.agg_smooth_cvd(cvd).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a8d163a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region1</th>\n",
       "      <th>region2</th>\n",
       "      <th>level_2</th>\n",
       "      <th>dist</th>\n",
       "      <th>n_valid.smoothed</th>\n",
       "      <th>balanced.sum.smoothed</th>\n",
       "      <th>balanced.avg.smoothed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19748.305903</td>\n",
       "      <td>23.002208</td>\n",
       "      <td>1.164769e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>19672.012304</td>\n",
       "      <td>1914.413783</td>\n",
       "      <td>9.731662e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>19607.556772</td>\n",
       "      <td>945.942439</td>\n",
       "      <td>4.824377e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>chr17:0-24714921</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>19556.911428</td>\n",
       "      <td>536.929382</td>\n",
       "      <td>2.745471e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325449</th>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>149050</td>\n",
       "      <td>149050</td>\n",
       "      <td>17234.604470</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>1.207903e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325450</th>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>149051</td>\n",
       "      <td>149051</td>\n",
       "      <td>17234.382187</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>1.207902e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325451</th>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>149052</td>\n",
       "      <td>149052</td>\n",
       "      <td>17234.159907</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>1.207902e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325452</th>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>149053</td>\n",
       "      <td>149053</td>\n",
       "      <td>17233.937632</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>1.207902e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325453</th>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>chr2:93139351-242193529</td>\n",
       "      <td>149054</td>\n",
       "      <td>149054</td>\n",
       "      <td>17233.715362</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>1.207901e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>325454 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        region1  ... balanced.avg.smoothed\n",
       "0              chr17:0-24714921  ...                   NaN\n",
       "1              chr17:0-24714921  ...          1.164769e-03\n",
       "2              chr17:0-24714921  ...          9.731662e-02\n",
       "3              chr17:0-24714921  ...          4.824377e-02\n",
       "4              chr17:0-24714921  ...          2.745471e-02\n",
       "...                         ...  ...                   ...\n",
       "325449  chr2:93139351-242193529  ...          1.207903e-07\n",
       "325450  chr2:93139351-242193529  ...          1.207902e-07\n",
       "325451  chr2:93139351-242193529  ...          1.207902e-07\n",
       "325452  chr2:93139351-242193529  ...          1.207902e-07\n",
       "325453  chr2:93139351-242193529  ...          1.207901e-07\n",
       "\n",
       "[325454 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvd_smoothed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "compliant-installation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 150\n",
    "\n",
    "# for reg in cvd.region1.unique():\n",
    "#     cvd_loc = cvd[cvd.region1 == reg]\n",
    "#     plt.loglog(\n",
    "#         cvd_loc['diag'],\n",
    "#         cvd_loc['balanced.sum'] / cvd_loc['n_valid']\n",
    "#     )\n",
    "\n",
    "for reg in cvd_smoothed.region1.unique():\n",
    "    cvd_loc = cvd_smoothed[cvd_smoothed.region1 == reg]\n",
    "\n",
    "    plt.loglog(\n",
    "        cvd_loc['dist'],\n",
    "        cvd_loc['balanced.avg.smoothed']\n",
    "    )\n",
    "ax = plt.gca()\n",
    "ax.set(aspect=1.0);\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "726a15e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign genome-wide P(s) to each region:\n",
    "cvd_smoothed_gw = cooltools.sandbox.expected_smoothing.agg_smooth_cvd(cvd, groupby=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39cc0e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 150\n",
    "\n",
    "\n",
    "plt.loglog(\n",
    "    cvd_smoothed_gw['dist'],\n",
    "    cvd_smoothed_gw['balanced.avg.smoothed']\n",
    ")\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set(aspect=1.0);\n",
    "ax.grid()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f29378d166744aaaed35a8c512e1253b2589c6d47f39e24c8ea5ce46f8c1fd5a"
  },
  "kernelspec": {
   "display_name": "aquarium_kernel",
   "language": "python",
   "name": "aquarium_kernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
